1c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// This file is part of Eigen, a lightweight C++ template library
2c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// for linear algebra.
3c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath//
4c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
5c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath//
6c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// This Source Code Form is subject to the terms of the Mozilla
7c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// Public License v. 2.0. If a copy of the MPL was not distributed
8c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
9c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
10c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#ifndef EIGEN_TRIANGULAR_SOLVER_MATRIX_H
11c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#define EIGEN_TRIANGULAR_SOLVER_MATRIX_H
12c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
13c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathnamespace Eigen {
14c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
15c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathnamespace internal {
16c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
17c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// if the rhs is row major, let's transpose the product
18c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate <typename Scalar, typename Index, int Side, int Mode, bool Conjugate, int TriStorageOrder>
19c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathstruct triangular_solve_matrix<Scalar,Index,Side,Mode,Conjugate,TriStorageOrder,RowMajor>
20c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{
217faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez  static void run(
22c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    Index size, Index cols,
23c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    const Scalar*  tri, Index triStride,
24c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    Scalar* _other, Index otherStride,
25c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    level3_blocking<Scalar,Scalar>& blocking)
26c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  {
27c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    triangular_solve_matrix<
28c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      Scalar, Index, Side==OnTheLeft?OnTheRight:OnTheLeft,
29c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      (Mode&UnitDiag) | ((Mode&Upper) ? Lower : Upper),
30c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      NumTraits<Scalar>::IsComplex && Conjugate,
31c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      TriStorageOrder==RowMajor ? ColMajor : RowMajor, ColMajor>
32c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      ::run(size, cols, tri, triStride, _other, otherStride, blocking);
33c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  }
34c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath};
35c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
36c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath/* Optimized triangular solver with multiple right hand side and the triangular matrix on the left
37c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath */
38c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate <typename Scalar, typename Index, int Mode, bool Conjugate, int TriStorageOrder>
39c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathstruct triangular_solve_matrix<Scalar,Index,OnTheLeft,Mode,Conjugate,TriStorageOrder,ColMajor>
40c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{
41c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  static EIGEN_DONT_INLINE void run(
42c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    Index size, Index otherSize,
43c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    const Scalar* _tri, Index triStride,
44c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    Scalar* _other, Index otherStride,
457faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez    level3_blocking<Scalar,Scalar>& blocking);
467faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez};
477faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandeztemplate <typename Scalar, typename Index, int Mode, bool Conjugate, int TriStorageOrder>
487faaa9f3f0df9d23790277834d426c3d992ac3baCarlos HernandezEIGEN_DONT_INLINE void triangular_solve_matrix<Scalar,Index,OnTheLeft,Mode,Conjugate,TriStorageOrder,ColMajor>::run(
497faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez    Index size, Index otherSize,
507faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez    const Scalar* _tri, Index triStride,
517faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez    Scalar* _other, Index otherStride,
52c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    level3_blocking<Scalar,Scalar>& blocking)
53c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  {
54c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    Index cols = otherSize;
55c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    const_blas_data_mapper<Scalar, Index, TriStorageOrder> tri(_tri,triStride);
56c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    blas_data_mapper<Scalar, Index, ColMajor> other(_other,otherStride);
57c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
58c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    typedef gebp_traits<Scalar,Scalar> Traits;
59c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    enum {
60c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      SmallPanelWidth   = EIGEN_PLAIN_ENUM_MAX(Traits::mr,Traits::nr),
61c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      IsLower = (Mode&Lower) == Lower
62c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    };
63c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
64c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    Index kc = blocking.kc();                   // cache block size along the K direction
65c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    Index mc = (std::min)(size,blocking.mc());  // cache block size along the M direction
66c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
67c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    std::size_t sizeA = kc*mc;
68c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    std::size_t sizeB = kc*cols;
69c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    std::size_t sizeW = kc*Traits::WorkSpaceFactor;
70c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
71c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    ei_declare_aligned_stack_constructed_variable(Scalar, blockA, sizeA, blocking.blockA());
72c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    ei_declare_aligned_stack_constructed_variable(Scalar, blockB, sizeB, blocking.blockB());
73c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    ei_declare_aligned_stack_constructed_variable(Scalar, blockW, sizeW, blocking.blockW());
74c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
75c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    conj_if<Conjugate> conj;
76c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    gebp_kernel<Scalar, Scalar, Index, Traits::mr, Traits::nr, Conjugate, false> gebp_kernel;
77c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    gemm_pack_lhs<Scalar, Index, Traits::mr, Traits::LhsProgress, TriStorageOrder> pack_lhs;
78c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    gemm_pack_rhs<Scalar, Index, Traits::nr, ColMajor, false, true> pack_rhs;
79c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
80c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    // the goal here is to subdivise the Rhs panels such that we keep some cache
81c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    // coherence when accessing the rhs elements
82c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    std::ptrdiff_t l1, l2;
83c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    manage_caching_sizes(GetAction, &l1, &l2);
84c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    Index subcols = cols>0 ? l2/(4 * sizeof(Scalar) * otherStride) : 0;
85c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    subcols = std::max<Index>((subcols/Traits::nr)*Traits::nr, Traits::nr);
86c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
87c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    for(Index k2=IsLower ? 0 : size;
88c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath        IsLower ? k2<size : k2>0;
89c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath        IsLower ? k2+=kc : k2-=kc)
90c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    {
91c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      const Index actual_kc = (std::min)(IsLower ? size-k2 : k2, kc);
92c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
93c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      // We have selected and packed a big horizontal panel R1 of rhs. Let B be the packed copy of this panel,
94c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      // and R2 the remaining part of rhs. The corresponding vertical panel of lhs is split into
95c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      // A11 (the triangular part) and A21 the remaining rectangular part.
96c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      // Then the high level algorithm is:
97c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      //  - B = R1                    => general block copy (done during the next step)
98c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      //  - R1 = A11^-1 B             => tricky part
99c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      //  - update B from the new R1  => actually this has to be performed continuously during the above step
100c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      //  - R2 -= A21 * B             => GEPP
101c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
102c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      // The tricky part: compute R1 = A11^-1 B while updating B from R1
103c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      // The idea is to split A11 into multiple small vertical panels.
104c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      // Each panel can be split into a small triangular part T1k which is processed without optimization,
105c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      // and the remaining small part T2k which is processed using gebp with appropriate block strides
106c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      for(Index j2=0; j2<cols; j2+=subcols)
107c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      {
108c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath        Index actual_cols = (std::min)(cols-j2,subcols);
109c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath        // for each small vertical panels [T1k^T, T2k^T]^T of lhs
110c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath        for (Index k1=0; k1<actual_kc; k1+=SmallPanelWidth)
111c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath        {
112c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath          Index actualPanelWidth = std::min<Index>(actual_kc-k1, SmallPanelWidth);
113c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath          // tr solve
114c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath          for (Index k=0; k<actualPanelWidth; ++k)
115c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath          {
116c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath            // TODO write a small kernel handling this (can be shared with trsv)
117c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath            Index i  = IsLower ? k2+k1+k : k2-k1-k-1;
118c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath            Index s  = IsLower ? k2+k1 : i+1;
119c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath            Index rs = actualPanelWidth - k - 1; // remaining size
120c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
121c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath            Scalar a = (Mode & UnitDiag) ? Scalar(1) : Scalar(1)/conj(tri(i,i));
122c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath            for (Index j=j2; j<j2+actual_cols; ++j)
123c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath            {
124c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath              if (TriStorageOrder==RowMajor)
125c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath              {
126c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath                Scalar b(0);
127c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath                const Scalar* l = &tri(i,s);
128c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath                Scalar* r = &other(s,j);
129c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath                for (Index i3=0; i3<k; ++i3)
130c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath                  b += conj(l[i3]) * r[i3];
131c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
132c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath                other(i,j) = (other(i,j) - b)*a;
133c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath              }
134c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath              else
135c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath              {
136c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath                Index s = IsLower ? i+1 : i-rs;
137c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath                Scalar b = (other(i,j) *= a);
138c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath                Scalar* r = &other(s,j);
139c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath                const Scalar* l = &tri(s,i);
140c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath                for (Index i3=0;i3<rs;++i3)
141c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath                  r[i3] -= b * conj(l[i3]);
142c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath              }
143c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath            }
144c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath          }
145c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
146c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath          Index lengthTarget = actual_kc-k1-actualPanelWidth;
147c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath          Index startBlock   = IsLower ? k2+k1 : k2-k1-actualPanelWidth;
148c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath          Index blockBOffset = IsLower ? k1 : lengthTarget;
149c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
150c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath          // update the respective rows of B from other
151c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath          pack_rhs(blockB+actual_kc*j2, &other(startBlock,j2), otherStride, actualPanelWidth, actual_cols, actual_kc, blockBOffset);
152c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
153c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath          // GEBP
154c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath          if (lengthTarget>0)
155c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath          {
156c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath            Index startTarget  = IsLower ? k2+k1+actualPanelWidth : k2-actual_kc;
157c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
158c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath            pack_lhs(blockA, &tri(startTarget,startBlock), triStride, actualPanelWidth, lengthTarget);
159c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
160c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath            gebp_kernel(&other(startTarget,j2), otherStride, blockA, blockB+actual_kc*j2, lengthTarget, actualPanelWidth, actual_cols, Scalar(-1),
161c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath                        actualPanelWidth, actual_kc, 0, blockBOffset, blockW);
162c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath          }
163c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath        }
164c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      }
165c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
166c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      // R2 -= A21 * B => GEPP
167c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      {
168c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath        Index start = IsLower ? k2+kc : 0;
169c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath        Index end   = IsLower ? size : k2-kc;
170c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath        for(Index i2=start; i2<end; i2+=mc)
171c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath        {
172c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath          const Index actual_mc = (std::min)(mc,end-i2);
173c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath          if (actual_mc>0)
174c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath          {
175c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath            pack_lhs(blockA, &tri(i2, IsLower ? k2 : k2-kc), triStride, actual_kc, actual_mc);
176c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
177c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath            gebp_kernel(_other+i2, otherStride, blockA, blockB, actual_mc, actual_kc, cols, Scalar(-1), -1, -1, 0, 0, blockW);
178c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath          }
179c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath        }
180c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      }
181c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    }
182c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  }
183c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
184c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath/* Optimized triangular solver with multiple left hand sides and the trinagular matrix on the right
185c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath */
186c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate <typename Scalar, typename Index, int Mode, bool Conjugate, int TriStorageOrder>
187c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathstruct triangular_solve_matrix<Scalar,Index,OnTheRight,Mode,Conjugate,TriStorageOrder,ColMajor>
188c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{
189c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  static EIGEN_DONT_INLINE void run(
190c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    Index size, Index otherSize,
191c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    const Scalar* _tri, Index triStride,
192c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    Scalar* _other, Index otherStride,
1937faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez    level3_blocking<Scalar,Scalar>& blocking);
1947faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez};
1957faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandeztemplate <typename Scalar, typename Index, int Mode, bool Conjugate, int TriStorageOrder>
1967faaa9f3f0df9d23790277834d426c3d992ac3baCarlos HernandezEIGEN_DONT_INLINE void triangular_solve_matrix<Scalar,Index,OnTheRight,Mode,Conjugate,TriStorageOrder,ColMajor>::run(
1977faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez    Index size, Index otherSize,
1987faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez    const Scalar* _tri, Index triStride,
1997faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez    Scalar* _other, Index otherStride,
200c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    level3_blocking<Scalar,Scalar>& blocking)
201c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  {
202c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    Index rows = otherSize;
203c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    const_blas_data_mapper<Scalar, Index, TriStorageOrder> rhs(_tri,triStride);
204c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    blas_data_mapper<Scalar, Index, ColMajor> lhs(_other,otherStride);
205c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
206c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    typedef gebp_traits<Scalar,Scalar> Traits;
207c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    enum {
208c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      RhsStorageOrder   = TriStorageOrder,
209c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      SmallPanelWidth   = EIGEN_PLAIN_ENUM_MAX(Traits::mr,Traits::nr),
210c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      IsLower = (Mode&Lower) == Lower
211c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    };
212c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
213c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    Index kc = blocking.kc();                   // cache block size along the K direction
214c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    Index mc = (std::min)(rows,blocking.mc());  // cache block size along the M direction
215c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
216c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    std::size_t sizeA = kc*mc;
217c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    std::size_t sizeB = kc*size;
218c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    std::size_t sizeW = kc*Traits::WorkSpaceFactor;
219c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
220c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    ei_declare_aligned_stack_constructed_variable(Scalar, blockA, sizeA, blocking.blockA());
221c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    ei_declare_aligned_stack_constructed_variable(Scalar, blockB, sizeB, blocking.blockB());
222c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    ei_declare_aligned_stack_constructed_variable(Scalar, blockW, sizeW, blocking.blockW());
223c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
224c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    conj_if<Conjugate> conj;
225c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    gebp_kernel<Scalar,Scalar, Index, Traits::mr, Traits::nr, false, Conjugate> gebp_kernel;
226c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    gemm_pack_rhs<Scalar, Index, Traits::nr,RhsStorageOrder> pack_rhs;
227c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    gemm_pack_rhs<Scalar, Index, Traits::nr,RhsStorageOrder,false,true> pack_rhs_panel;
228c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    gemm_pack_lhs<Scalar, Index, Traits::mr, Traits::LhsProgress, ColMajor, false, true> pack_lhs_panel;
229c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
230c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    for(Index k2=IsLower ? size : 0;
231c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath        IsLower ? k2>0 : k2<size;
232c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath        IsLower ? k2-=kc : k2+=kc)
233c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    {
234c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      const Index actual_kc = (std::min)(IsLower ? k2 : size-k2, kc);
235c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      Index actual_k2 = IsLower ? k2-actual_kc : k2 ;
236c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
237c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      Index startPanel = IsLower ? 0 : k2+actual_kc;
238c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      Index rs = IsLower ? actual_k2 : size - actual_k2 - actual_kc;
239c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      Scalar* geb = blockB+actual_kc*actual_kc;
240c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
241c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      if (rs>0) pack_rhs(geb, &rhs(actual_k2,startPanel), triStride, actual_kc, rs);
242c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
243c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      // triangular packing (we only pack the panels off the diagonal,
244c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      // neglecting the blocks overlapping the diagonal
245c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      {
246c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath        for (Index j2=0; j2<actual_kc; j2+=SmallPanelWidth)
247c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath        {
248c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath          Index actualPanelWidth = std::min<Index>(actual_kc-j2, SmallPanelWidth);
249c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath          Index actual_j2 = actual_k2 + j2;
250c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath          Index panelOffset = IsLower ? j2+actualPanelWidth : 0;
251c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath          Index panelLength = IsLower ? actual_kc-j2-actualPanelWidth : j2;
252c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
253c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath          if (panelLength>0)
254c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath          pack_rhs_panel(blockB+j2*actual_kc,
255c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath                         &rhs(actual_k2+panelOffset, actual_j2), triStride,
256c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath                         panelLength, actualPanelWidth,
257c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath                         actual_kc, panelOffset);
258c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath        }
259c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      }
260c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
261c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      for(Index i2=0; i2<rows; i2+=mc)
262c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      {
263c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath        const Index actual_mc = (std::min)(mc,rows-i2);
264c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
265c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath        // triangular solver kernel
266c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath        {
267c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath          // for each small block of the diagonal (=> vertical panels of rhs)
268c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath          for (Index j2 = IsLower
269c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath                      ? (actual_kc - ((actual_kc%SmallPanelWidth) ? Index(actual_kc%SmallPanelWidth)
270c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath                                                                  : Index(SmallPanelWidth)))
271c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath                      : 0;
272c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath               IsLower ? j2>=0 : j2<actual_kc;
273c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath               IsLower ? j2-=SmallPanelWidth : j2+=SmallPanelWidth)
274c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath          {
275c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath            Index actualPanelWidth = std::min<Index>(actual_kc-j2, SmallPanelWidth);
276c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath            Index absolute_j2 = actual_k2 + j2;
277c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath            Index panelOffset = IsLower ? j2+actualPanelWidth : 0;
278c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath            Index panelLength = IsLower ? actual_kc - j2 - actualPanelWidth : j2;
279c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
280c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath            // GEBP
281c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath            if(panelLength>0)
282c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath            {
283c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath              gebp_kernel(&lhs(i2,absolute_j2), otherStride,
284c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath                          blockA, blockB+j2*actual_kc,
285c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath                          actual_mc, panelLength, actualPanelWidth,
286c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath                          Scalar(-1),
287c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath                          actual_kc, actual_kc, // strides
288c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath                          panelOffset, panelOffset, // offsets
289c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath                          blockW);  // workspace
290c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath            }
291c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
292c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath            // unblocked triangular solve
293c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath            for (Index k=0; k<actualPanelWidth; ++k)
294c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath            {
295c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath              Index j = IsLower ? absolute_j2+actualPanelWidth-k-1 : absolute_j2+k;
296c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
297c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath              Scalar* r = &lhs(i2,j);
298c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath              for (Index k3=0; k3<k; ++k3)
299c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath              {
300c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath                Scalar b = conj(rhs(IsLower ? j+1+k3 : absolute_j2+k3,j));
301c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath                Scalar* a = &lhs(i2,IsLower ? j+1+k3 : absolute_j2+k3);
302c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath                for (Index i=0; i<actual_mc; ++i)
303c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath                  r[i] -= a[i] * b;
304c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath              }
305c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath              Scalar b = (Mode & UnitDiag) ? Scalar(1) : Scalar(1)/conj(rhs(j,j));
306c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath              for (Index i=0; i<actual_mc; ++i)
307c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath                r[i] *= b;
308c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath            }
309c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
310c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath            // pack the just computed part of lhs to A
311c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath            pack_lhs_panel(blockA, _other+absolute_j2*otherStride+i2, otherStride,
312c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath                           actualPanelWidth, actual_mc,
313c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath                           actual_kc, j2);
314c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath          }
315c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath        }
316c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
317c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath        if (rs>0)
318c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath          gebp_kernel(_other+i2+startPanel*otherStride, otherStride, blockA, geb,
319c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath                      actual_mc, actual_kc, rs, Scalar(-1),
320c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath                      -1, -1, 0, 0, blockW);
321c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      }
322c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    }
323c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  }
324c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
325c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} // end namespace internal
326c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
327c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} // end namespace Eigen
328c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
329c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#endif // EIGEN_TRIANGULAR_SOLVER_MATRIX_H
330